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Our convergence analyses guarantee that the algorithm weakly converges to a solution under certain assumptions.
Our convergence analysis shows that the resulting strategy provably converges to the solution of the dispatch problem starting from any initial power allocation, and therefore does not require any specific procedure for initialization.
Our convergence results depend on the following assumption.
We need the following known results for our convergence analysis.
Our convergence theorem is applied to the convex minimization problem.
Parabolicity is additionally confirmed by our convergence result for iterativemethods.
Next, we prove our convergence theorems as follows.
We also discuss the extension of our convergence guarantees to dynamically changing topologies.
Our convergence tests indicate that even small-sized SQSs can give reliable results.
Building a successful business that relies on both hardware and software is where we see aspects of our convergence.
In order to establish our convergence theorems, we need the following concepts.
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CEO of Professional Science Editing for Scientists @ prosciediting.com